MIMO Detector Lattice Triangularization Complexity
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Solution Overview
Problem
Current multiple antenna communication systems face challenges in detecting multiple sources corrupted by noise in MIMO fading channels and generating bit soft output information for external decoders, leading to performance degradation and high computational complexity.
Innovation Solution
A method and apparatus that detect sequences of digitally modulated symbols using a novel lattice representation and triangularization of the channel matrix, allowing for efficient computation of near-optimal extrinsic bit soft-output information, which is then fed back for iterative decoding, reducing complexity and latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Maximum-A-Posteriori (MAP) detection is used to achieve high-performance detection in MIMO fading channels, then detection accuracy is improved, but computational complexity increases significantly
Solution Approach 1:
The patent segments the complex MIMO detection problem into multiple processing stages: channel estimation, signal detection, and iterative decoding. By dividing the detection process into manageable components that can be processed separately and iteratively, the computational complexity is reduced while maintaining detection accuracy through multiple refinement passes.
Solution Approach 2:
The patent implements dynamic iterative detection where the detection process adapts through multiple iterations with feedback from the outer decoder. The system dynamically adjusts detection parameters and reprocesses signals with updated a-priori information, allowing accurate detection to be achieved progressively rather than requiring all computations upfront.
2Reliability
If iterative detection and decoding schemes are implemented to generate soft-output information, then performance is improved, but processing time and latency increase
Solution Approach 1:
The patent performs preliminary channel estimation and initial signal detection before iterative decoding begins. By pre-processing the received signals and preparing a-priori information in advance, the iterative process can converge faster with fewer iterations, reducing overall processing time while maintaining performance improvements.
Solution Approach 2:
The patent implements early termination of iterative processing when convergence criteria are met. Instead of always completing a fixed number of iterations, the system skips remaining iterations once sufficient accuracy is achieved, significantly reducing processing time for cases where full convergence is not necessary.
3Reliability
If soft-output information is generated for outer error-correction-code decoders, then error correction performance is improved, but device complexity increases
Solution Approach 1:
The patent designs a universal soft-output detector that can serve multiple functions: initial detection, iterative refinement, and information generation for outer decoders. By creating a multi-functional detection module that produces soft-output information as a natural byproduct of its operation, the system achieves improved error correction without requiring separate dedicated complexity for soft-output generation.
Solution Approach 2:
The patent implements feedback loops where soft-output information from the detector is passed to outer decoders, which in turn provide a-priori information back to the detector for subsequent iterations. This feedback mechanism improves error correction performance by allowing the system to refine detections based on code constraints without requiring fundamentally more complex detection hardware.
Data Source
AI summary
An embodiment of an arrangement detects sequences of digitally modulated symbols from multiple sources. The arrangement identifies a suitable set of candidate values for at least one transmitted sequence of symbols and determines for each candidate value a set of sequences of transmitted symbols. The arrangement estimates at least one further set of sequences of transmitted symbols, calculates a metric for each sequence of transmitted symbols, and selects the sequence that maximizes the metric. At the end, a-posteriori bit soft output information for the selected sequence is calculated from the metrics for said sequences. Generally, these calculations are based on the information coming from a channel-state-information matrix and a-priori information on the modulated symbols from a second module, such as a forward error-correction-code (ECC) decoder.